CVE-2026-57137

HIGHPre-NVD 8.88.8
EchelonGraph scoreLOW confidence

This high-severity CVE scores 8.8 under the CNA's CVSS (NVD's own analysis pending). EPSS exploit-prediction score not yet available (the EPSS model rescores nightly; freshly-published CVEs typically appear within 48 hours). GitHub Security Advisory data not yet ingested — confidence will rise once GHSA publishes (typical lag: hours to days for open-source ecosystem CVEs; never for infrastructure-only CVEs).

Triggered by: NVD CVSS baseline
Sources: cna:github_m
8.8EG
EchelonGraph verdictPlan a fixSerious severity, but no confirmed exploitation yet.
  • High severity, but no confirmed exploitation yet
CISA-KEV: Not listedEPSS PROB: CVSS: 8.8Exploit: None knownExposed: 0

No vendor fix yet — apply a workaround or compensating control (WAF / firewall / segmentation) and watch for a patch.

npm PraisonAI AgentLoop onToolCall approval runs after tool execution

Summary

The published npm package praisonai exports createAgentLoop(), whose onToolCall callback is documented and exampled as an approval hook. The implementation calls PraisonAI's generateText() wrapper with the caller's executable tools first, receives toolResults, and only then calls onToolCall().

Because AI SDK generateText() executes tools with an execute function as part of the generation call, onToolCall can deny a tool only after the sensitive side effect has already happened. PraisonAI then returns finishReason: "tool_rejected", which is a false security signal: the rejected tool already ran.

The PoV is deterministic and local-only. It uses mock AI SDK modules, no live model call, no API key, and no network target. The tool increments an in-memory counter rather than touching the filesystem or executing commands.

Technical Details

In src/praisonai-ts/src/ai/agent-loop.ts, the public config says:

/** On tool call callback (for approval) */
onToolCall?: (toolCall: ToolCallInfo) => Promise;

The inline approval example also asks a user for approval and returns the decision:

onToolCall: async (toolCall) => {
  const approved = await askUserForApproval(toolCall);
  return approved;
}

However, AgentLoop.step() calls generateText() with the executable tools before invoking onToolCall:

const result = await generateText({
  model: this.config.model,
  messages: this.messages as any,
  tools: this.config.tools,
  maxSteps: 1,
});

It then materializes toolResults:

toolResults: result.toolResults.map(tr => ({
  toolCallId: tr.toolCallId,
  toolName: tr.toolName,
  result: tr.result,
})),

Only afterward does the approval callback run:

if (this.config.onToolCall) {
  for (const toolCall of step.toolCalls) {
    const approved = await this.config.onToolCall(toolCall);
    if (!approved) {
      this.complete = true;
      step.finishReason = 'tool_rejected';
      break;
    }
  }
}

src/praisonai-ts/src/ai/generate-text.ts forwards the caller's tools directly to AI SDK:

const result = await sdk.generateText({
  model,
  ...
  tools: options.tools,
  maxSteps: options.maxSteps,
  ...
});

AI SDK documents that generateText() "generates text and calls tools", and that tools with an execute function run automatically unless approval is handled before execution with needsApproval.

The published npm:[email protected] dist files preserve the same order:

  • dist/ai/agent-loop.js lines 150-157 call generateText() with executable tools.
  • lines 162-171 materialize toolResults.
  • lines 183-195 call onToolCall() and set tool_rejected afterward.

Why This Is Not Intended Behavior

This is not a trust-model-only issue. PraisonAI explicitly labels onToolCall as an approval callback and shows an approval example. A user who returns false from that callback expects the tool not to run.

It also conflicts with the AI SDK execution model PraisonAI wraps:

  • AI SDK generateText() executes tools that include an execute function.
  • AI SDK approval is a pre-execution boundary (needsApproval), not a post-execution notification.
  • AI SDK loop control documentation treats "a tool call needs approval" as a condition that stops or pauses the loop before executing the tool.

PraisonAI's current behavior instead creates a post-execution audit hook while naming and documenting it as approval.

PoV

Run from a local reproduction checkout:

node poc/pov_poc.js 1.7.1

Expected output includes:

{
  "praisonaiVersion": "1.7.1",
  "createAgentLoopExported": true,
  "eventOrder": ["tool-executed", "approval-denied"],
  "sideEffects": 1,
  "finishReason": "tool_rejected",
  "toolCallCount": 1,
  "toolResultCount": 1,
  "rejectedAfterExecution": true,
  "vulnerable": true,
  "patchedControl": {
    "order": ["approval-denied"],
    "sideEffects": 0,
    "toolCallCount": 1,
    "toolResultCount": 0,
    "blocksBeforeExecution": true
  }
}

The PoV installs npm:[email protected] into a temporary project and supplies mock ai and @ai-sdk/openai modules. The mocked generateText() returns one tool-call intent and executes a supplied execute handler if present. This keeps the proof deterministic and isolates PraisonAI's ordering bug.

The vulnerable run uses createAgentLoop() with:

  • a dangerousWrite tool whose execute() handler increments an in-memory side-effect counter and records tool-executed;
  • an onToolCall approval callback that always returns false and records approval-denied.

The observed order is:

tool-executed > approval-denied

That proves denial happens after execution. The toolResults array contains the tool's result even though PraisonAI reports finishReason: "tool_rejected".

The patched-control comparison strips executable handlers before the model step, requests approval on the tool-call intent, and only executes if approval succeeds. With the same denial decision, the control output is:

approval-denied
sideEffects = 0
toolResultCount = 0

PoC

The PoV section above contains the local reproduction command, input, and decisive output.

Impact

Any application using npm PraisonAI createAgentLoop() with onToolCall as a human-in-the-loop or policy approval boundary can execute denied tools.

If the application exposes the agent loop to lower-trust prompts or users and registers powerful tools, an attacker can cause the model to call a tool that the approval callback denies. The denial occurs too late. Depending on the registered tool, impact can include file modification, command execution, external API calls, data mutation, credential use, or other side effects with the privileges of the PraisonAI process.

The report does not claim that npm PraisonAI exposes this as a default network service. It is a library-level approval-boundary bypass in the exported TypeScript agent-loop API.

Severity

Suggested severity: High.

Rationale:

  • AV: common deployment pattern is an application exposing agent prompts over a network.
  • AC: attacker only needs to induce a tool call.
  • PR: conservative base score assumes the attacker can submit prompts to the application.
  • UI: no additional operator action is needed for the tool to execute before denial; even a denial callback is too late.
  • S: impact is in the PraisonAI-hosting application process.
  • C/I/A: depends on registered tools; shell/file/API tools can affect confidentiality, integrity, and availability.

If maintainers score only local scripts that process untrusted repositories or prompts, AV:L may be reasonable. If they score public unauthenticated prompt endpoints built on this API, PR:N may be reasonable.

Suggested Fix

Do not pass executable tool handlers into generateText() before approval.

One safe shape:

  • Convert configured tools into intent-only tool definitions without execute.
  • Call generateText() to obtain the model's tool-call intent.
  • Invoke onToolCall(toolCall) before any side effect.
  • Execute the selected tool only if approval returns true.
  • Append approved tool results to the conversation and continue the loop.

Alternatively, if PraisonAI wants to delegate approval to AI SDK v6, translate onToolCall into per-tool needsApproval semantics so AI SDK pauses before calling execute.

Regression tests should include:

  • onToolCall returns false and the tool execute() counter remains zero;
  • onToolCall returns true and the tool executes exactly once;
  • tool_rejected is never reported together with a tool result produced by the denied tool;
  • streaming and non-streaming loop variants use the same approval ordering if added later.

Affected Package/Versions

  • Repository: MervinPraison/PraisonAI
  • Package: npm:praisonai
  • Component: TypeScript AgentLoop
  • Current head validated: 1ad58ca02975ff1398efeda694ea2ab78f20cf3e
  • Current tag validated: v4.6.58
  • Latest npm package validated: 1.7.1

Suggested affected range:

npm:praisonai >= 1.4.0, <= 1.7.1

Selected version sweep:

  • 1.0.0: package main cannot be required in the selected test environment.
  • 1.2.0: createAgentLoop is not exported.
  • 1.3.6: createAgentLoop is not exported.
  • 1.4.0: vulnerable.
  • 1.5.0: vulnerable.
  • 1.5.4: vulnerable.
  • 1.6.0: vulnerable.
  • 1.7.0: vulnerable.
  • 1.7.1: vulnerable.

Advisory History

This is distinct from known and previously submitted PraisonAI issues:

  • GHSA-ffp3-3562-8cv3 covers Python praisonaiagents approval cache keyed by tool name rather than invocation arguments.
  • GHSA-qwgj-rrpj-75xm covers Python Chainlit UI overriding configured approval mode with auto.
  • GHSA-63v4-w882-g4x2 / poc covers Python HTTPApproval approval-page XSS.
  • poc covers npm TypeScript AgentOS missing authentication.
  • poc covers npm TypeScript codeMode sandbox escape.
  • poc covers npm TypeScript MCPServer missing authentication.

No visible local or GitHub advisory covers npm TypeScript AgentLoop.onToolCall executing after tool results already exist.

CVSS v3
8.8
EG Score
8.8(low)
EG Risk
44(Track)
EG Risk 44/100SSVC: Track

EG Risk is EchelonGraph's 0–100 priority score: it fuses intrinsic severity with real-world exploitation and automatability so you can rank equal-severity CVEs and fix the most dangerous first. Higher = act sooner. Distinct from the 0–10 EG Score (severity).

How it’s computed
Severity88% × 45%
Exploitation0% × 40%
Automatability30% × 15%
Action: Routine — remediate on your standard cadence.
EPSS PROB
EPSS %ILE
KEV
Not listed

Published

June 18, 2026

Last Modified

June 18, 2026

Vendor Advisories for CVE-2026-57137(1)

These vendors published their own advisory mentioning this CVE — often with vendor-specific remediation steps + affected product lists not in NVD.

Affected Packages

(1 across 1 ecosystem)
npm(1)
PackageVulnerable rangeFixed inDependents
praisonai1.7.2

Data Freshness Timeline

(refreshed 3× in last 7d / 3× in last 30d)

Each row is a source pipeline that fetched or updated this CVE on that date, with what changed. For example, "NVD update" means NVD published or revised its analysis for this CVE; "MITRE cvelistV5" means we ingested or refreshed it from the CNA feed. Most recent first.

  1. 2026-07-26 17:45 UTCEG score recompute
  2. 2026-07-23 03:20 UTCEG score recompute
  3. 2026-07-20 21:33 UTCEG score recompute

Frequently asked(4)

What is CVE-2026-57137?
CVE-2026-57137 is a high vulnerability published on June 18, 2026. npm PraisonAI AgentLoop onToolCall approval runs after tool execution Summary The published npm package praisonai exports createAgentLoop(), whose onToolCall callback is documented and exampled as an approval hook. The implementation calls PraisonAI's generateText() wrapper with the caller's…
When was CVE-2026-57137 disclosed?
CVE-2026-57137 was first published in the National Vulnerability Database on June 18, 2026. EchelonGraph re-ingests CVE updates from NVD on a 2-hour cycle, so this page reflects the latest published state.
What is the CVSS score of CVE-2026-57137?
CVE-2026-57137 has a CVSS v4.0 base score of 8.8 (CNA self-assessment; NVD's own analysis pending). The EG score is currently aggregating — additional source signals are being incorporated as they become available..
How do I remediate CVE-2026-57137?
Patch to the fixed version published by the affected vendor. Where vendor advisories exist for CVE-2026-57137, EchelonGraph cross-links them in the Vendor Advisories panel below — those typically contain the canonical remediation steps, fixed version numbers, and any vendor-specific mitigations.

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